Part One of Three
There has been a not so quiet revolution going on in an industry that affects every person in North America, and the long-term consequences for the employment of humans (vs. intelligent machines) in that industry are not rosy. Every human worker that is displaced in this industry is, and not without a full measure of irony, a lost consumer.
This is the first of three columns devoted to these changes and their implications. In this first part, I will paint the “big picture” and in the subsequent parts, I will drill into the specifics of the acquired technologies that are disruptive.
Beginning around 2011, the year the economy really started to recover from the financial meltdown of 2008, companies that comprised the ecosystem called “The Supply Chain” have been spending heavily for technology to improve operational efficiencies. Taking just supply chain management software as a benchmark, companies spent about $10 billion in 2014, a 12% increase over 2013 spending, and the highest annual growth rate since 2011, according to a study by Gartner. If we extend our perspective back to the start of the crisis in 2008, there is a clear “take off” point around 2011, leading to the current acquisition pace.
BI (business intelligence), derived from the collection and analysis of “big data”, is rapidly rising to prominence among supply-chain professionals. The word increasingly being used to almost gleefully describe the impact of BI on business operations is “disruptive.”
What is being most disrupted is the traditional role that people play in supply chain operations. From temporary worker to operations executives, people are at risk of being replaced by automated systems that take advantage of advances in the Industrial Internet. Smart machines promise advances in operational productivity—and profitability.
One of the components of the supply chain is warehousing, which in today’s parlance is better described as distribution center operations. Think of a DC (distribution center) as a hub, much like a major airport such as Chicago’s O’Hare. Many people from different smaller cities or other major hubs arrive on planes operated by different carriers, using O’Hare as a transit point to get on another flight to somewhere. They come in, and in a very short time go back out. The “mix” of people on inbound planes is different from that on the outbound plane.
Distribution centers function very much in the same way. Located close to population centers and, usually at the crossroads of interstate highways, trucks, and rail cars operated by different carriers deliver products made by many manufacturers elsewhere in the country. The products are sorted, stored, and then “mixed” to fulfill orders, which are then shipped to customers in the population center served by the DC. Like O’Hare, a DC is a hub intermediating the exchange of products, the origin, and destination of which are highly variable.
For many years leading up to the turn of the century, especially among retailers, “back of the house” capital improvement projects fared poorly in the annual budgeting process. Money was concentrated on the “front of the house” and from a software perspective. Enhancing the customer experience was the first priority for capital project approval. Distribution center operations were kept “status quo”, meaning that mostly paper-based manual processes continued to be used, and the solution to keep on top of growing volume was to add more people. Seasonal workers were hired in the tens of thousands, with one outcome being the creation of a caravan workforce.
The introduction of wireless RF (radio frequency) scanning techniques produced some measure of productivity improvement, but the baseline unit of work—the human—could only be enhanced by this type of technology.
Putting the money into customer engagement technology made sense because the customer was increasingly capable of shopping online (eCommerce), and to use smartphones to search for the best price for an item even when standing and looking at it in a store (mCommerce giving rise to “Showrooming”).
As companies struggled to keep up with their customers’ shift to non-traditional ways of buying, there was recognition these new techniques were changing how distribution centers had to handle the fulfillment of non-traditional orders. Looking at a distribution center from the outside, you will see a big, boxy structure with a large number of doors where trucks are backed up. Step inside, and if it is your first time in one, you are overwhelmed by the sight of products stacked in massive rack systems reaching to the ceiling, forklift trucks zooming by you in what appears to be semiorganized chaos, and noise. Distribution centers are very noisy places.
It then dawns on you just how much it must have cost to set up the infrastructure. If it was set up under the traditional business model of shipping pallet loads of products to stores where individual items were taken out of cartons and put on shelves for the customer to buy, then you understand that operational efficiency measurements were designed to measure productivity for that model. Change the model, as eCommerce and mCommerce have, and the old metrics become irrelevant.
With the advent of eCommerce, the retail store is not the final destination; rather, the customer is. The DC is now involved with direct fulfillment. This changes the customer engagement outcome because direct order fulfillment errors, which stores made transparent to the customer, now put the brand image at risk. The DC now has to open cartons from an operations persepctive, pick one or two items from a subset of the available racking (the level that a human can reach without a forklift), audit the picks to ensure accuracy, and package them for a UPS or FedEx shipment. The operational profile has not only changed, it has become inefficient. With that, you need humans to process a higher volume of smaller orders.
Then along came Amazon with same day shipping. Robotic warehouses and artificial intelligence applied to customer decision-making, collectively having a massively disruptive impact on its supply chain. I contend that for any market Amazon has entered, it has either galvanized action among its competitors or forced them out of business.
Just one example you have probably not considered: Amazon providing same day wine and liquor sales and delivery. Wine and liquor distribution is one of the most traditional and highly regulated industries you can find. Yet Amazon’s entry has forced a reaction from the industry, one focused on taking eCommerce orders directly from consumers. The industry’s largest trade group, The Wine & Spirits Wholesalers of America, bought a minority stake in Drizly Inc., which has received a direct infusion of capital to take on Amazon for eCommerce dominance in the U.S.
The direct effect of increased direct-to-consumer sales on DC operations is staggering. The traditional operational profile of the industry is to ship case quantities of product to retail stores. With the transition to eCommerce fulfillment, the number of individual bottle picks that are typical of a consumer order increases significantly, and most distributors have little to no infrastructure set in place for this transition.
In the next column, we’ll explore just how eCommerce, mCommerce, and omnichannel commerce is changing DC operations, and how automation will improve supply chain performance while reducing the number of DC workers. We’ll show how the rise of smartphones has driven massive and radical new fulfillment strategies at the “back of the house.”
Tim Lindner is senior business consultant with a software company, and a regular contributor to Connected World. He can be reached at firstname.lastname@example.org